ct 20(25): e3

Research Article

Non-Redundant Contour Directional Feature Vectors for Character Recognition

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  • @ARTICLE{10.4108/eai.19-11-2020.167204,
        author={Tusar Kanti Mishra and Sandeep Panda and Banshidhar Majhi},
        title={Non-Redundant Contour Directional Feature Vectors for Character Recognition},
        journal={EAI Endorsed Transactions on Creative Technologies},
        keywords={OCR, Odia character recognition, pattern recognition, classification, feature extraction},
  • Tusar Kanti Mishra
    Sandeep Panda
    Banshidhar Majhi
    Year: 2020
    Non-Redundant Contour Directional Feature Vectors for Character Recognition
    DOI: 10.4108/eai.19-11-2020.167204
Tusar Kanti Mishra1,*, Sandeep Panda2, Banshidhar Majhi3
  • 1: Department of CSE, GITAM Deemed to be University, Visakhapatnam, India, 530045
  • 2: Goldman Sachs, Bengaluru, India
  • 3: Department of CSE, IIIT-DM, Kancheepuram, Chennai, India
*Contact email: tusar.k.mishra@gmail.com


This paper presents a novel shape based feature for printed character recognition. The shape features are derived from the contour of the character which is unique to all characters. Preprocessing is performed to standardize the characters and handle all variations such as bold, italics and bold-italics font characteristics. The complete character set is clustered into different groups based on contour feature. A probe character is mapped into the corresponding cluster prior to recognition. This helps to reduce the computational overhead. Finally two recognition schemes have been proposed, based on angle feature extracted from the contour information and a longest common substring (LCS) based feature. Simulation has been carried out to validate the efficacy of the proposed scheme on printed Odia characters. Performance accuracy has been compared with the existing schemes. In general, it is observed that the proposed scheme outperforms the competitive schemes.